As the image processing devices have become faster and more precise, the amount of image data to be handled has increased in conventional cases. On the other hand, there is a high degree of necessity for quantization processes for digital image signals, because of regulations or physical constraints on communication bands, memory capacities, circuit sizes, and the like. As a result, quantization processes are performed before communication, memory, large-scale circuits, and the like. It should be noted that, in many cases, a quantization process is performed during a lower-bit rounding process, so as to cope with the increased speed of the image processing device at the preceding stage, or cope with the versatility of the image processing device at the receiving end or the reading end, for example.
Meanwhile, enhanced functions are required in image processing these days, and more and more cases involve image processing such as a noise reduction process and a digital gain process for coping with low illuminance, and a dynamic range expansion process and a gradation conversion process for coping with high dynamic ranges.
However, in image processing such as a noise reduction process, a digital gain process, a gradation conversion process, and a dynamic range expansion process, there exist performance limitations due to bit precision. Therefore, the image processing device at the subsequent stage having bit precision quantized to conform to the communication band, the memory capacity, or a large-scale circuit needs to perform image processing with the quantized bit precision, and is unable to exhibit sufficient performance.
It should be noted that, in the image processing device at the preceding stage that is required to have a higher speed, it is difficult to execute a noise reduction function and a gradation conversion process that require a long processing time. Further, to restore the bit precision of the image processing device at the subsequent stage, an inverse quantization process needs to be performed. However, quantization through a lower-bit rounding process is a lossy process, and therefore, the generated quantization error cannot be restored.
For example, Patent Document 1 discloses an image decoding device capable of stably reducing strangeness and unclarity caused by truncation of bit planes.